OBJECTIVE: A major proportion of RR interval variability in long-term recordings is due to slow (< 0.03 Hz) fluctuations, which seem to be a good predictor of survival after myocardial infarction, whose origin remains unclear. METHODS: To study the effect of physical activity we compared by spectral analysis of the RR interval in 10 healthy human subjects (aged 28[s.e. 2] years) during 1-h periods each of rest (no activity), alternating rest and mild exercise (rhythmic activity), and normal spontaneous (random) activity. RESULTS: Compared to rest, during both random and rhythmic activities, the RR variance increased significantly (from 5802[1030] to 13388[1448] ms2, P < 0.05, and to 24959[2901], P < 0.001) due to an increase in power below 0.03 Hz (from 3017[467] to 9606[966] ms2, P < 0.01, and to 21 103[2298] ms2, P < 0.001) which explained 55.4, 73.2 and 86.1% of total RR variance, respectively. CONCLUSIONS: The amount of RR variability and its slower fluctuations largely depend on physical activity, regardless of its regular or irregular occurrence. Attempts to predict cardiovascular prognosis on the basis of RR fluctuations should therefore take account of the confounding effect of physical activity since healthier subjects would probably be more active.
OBJECTIVE: A major proportion of RR interval variability in long-term recordings is due to slow (< 0.03 Hz) fluctuations, which seem to be a good predictor of survival after myocardial infarction, whose origin remains unclear. METHODS: To study the effect of physical activity we compared by spectral analysis of the RR interval in 10 healthy human subjects (aged 28[s.e. 2] years) during 1-h periods each of rest (no activity), alternating rest and mild exercise (rhythmic activity), and normal spontaneous (random) activity. RESULTS: Compared to rest, during both random and rhythmic activities, the RR variance increased significantly (from 5802[1030] to 13388[1448] ms2, P < 0.05, and to 24959[2901], P < 0.001) due to an increase in power below 0.03 Hz (from 3017[467] to 9606[966] ms2, P < 0.01, and to 21 103[2298] ms2, P < 0.001) which explained 55.4, 73.2 and 86.1% of total RR variance, respectively. CONCLUSIONS: The amount of RR variability and its slower fluctuations largely depend on physical activity, regardless of its regular or irregular occurrence. Attempts to predict cardiovascular prognosis on the basis of RR fluctuations should therefore take account of the confounding effect of physical activity since healthier subjects would probably be more active.
Authors: B M Pluim; C A Swenne; A H Zwinderman; A C Maan; A van der Laarse; J Doornbos; E E Van der Wall Journal: Heart Date: 1999-06 Impact factor: 5.994
Authors: Carlos E D'Negri; Liliana Marelich; Daniel Vigo; Rafael S Acunzo; Luis A Girotti; Daniel P Cardinali; Leonardo Nicola Siri Journal: Clin Auton Res Date: 2005-06 Impact factor: 4.435
Authors: S Boveda; M Galinier; A Pathak; J Fourcade; B Dongay; D Benchendikh; P Massabuau; J M Fauvel; J M Senard; J P Bounhoure Journal: J Interv Card Electrophysiol Date: 2001-06 Impact factor: 1.900
Authors: Julia D Blood; Jia Wu; Tara M Chaplin; Rebecca Hommer; Lauren Vazquez; Helena J V Rutherford; Linda C Mayes; Michael J Crowley Journal: J Affect Disord Date: 2015-07-26 Impact factor: 4.839